A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory
Abstract: Model-based based analysis tools, built on assumptions and simplifications, are difficult to handle smart grids with data characterized by volume, velocity, variety, and veracity (i.e., 4Vs data). This paper, using random matrix theory (RMT), motivates data-driven en tools to perceive the complex grids in high high-dimension; dimension; meanwhile, an architecture with detailed procedures is proposed. In algorithm perspective, the architecture performs a high high-dimensional dimensional analysis and compares the findings with RMT predictions to conduct duct anomaly detections. Mean spectral radius (MSR), as a statistical indicator, is defined to reflect the correlations of system data in different dimensions. In management mode perspective, a group group--work mode is discussed for smart grids operation. This m mode ode breaks through regional limitations for energy flows and data flows, and makes advanced big data analyses possible. For a specific large large-scale zone-dividing dividing system with multiple connected utilities, each site, operating under the group group-work work mode, is able ab to work out the regional MSR only with its own measured/simulated data. The largelarge scale interconnected system, in this way, is naturally decoupled from statistical parameters perspective, rather than from engineering models perspective. Furthermore, a comparative omparative analysis of these distributed MSRs, even with imperceptible different raw data, will produce a contour line to detect the event and locate the source. It demonstrates that the architecture is compatible with